The predictive power of serum vitamin D for poor outcomes in COVID‐19 patients
Bibliographic record
Abstract
Abstract Considering the high prevalence of vitamin D deficiency worldwide and its relationship with immune response to viral infections, this study attempted to identify the predictive power of serum vitamin D for poor outcomes among the COVID‐19 patients. This retrospective cohort study included all patients with confirmed COVID‐19 hospitalized between February 20, 2020, and April 20, 2020, at a designated COVID‐19 hospital, located in Tehran province, Iran. General characteristics, medical history and clinical symptoms were recorded by trained physicians. Blood parameters including complete blood count, creatinine, lactate dehydrogenase, creatine phosphokinase, erythrocyte sedimentation rate, C‐reactive protein and vitamin D were tested. This study included 290 hospitalized patients with COVID‐19 (the mean age [SD]: 61.6 [16.9], 56.6% males), of whom 142 had vitamin D concentrations less than 20 ng/ml, defined as vitamin D deficiency. COVID‐19 patients with vitamin D deficiency were more likely to die (Crude OR [95% CI]: 2.30 [1.25–4.26]), require ICU (2.06 [1.22–3.46]) and invasive mechanical ventilation (2.03 [1.04–3.93]) based on univariate logistic regression results. Although, after adjusting for potentials confounders such as gender and age, the association between vitamin D and need to invasive mechanical ventilation lost its significance, adjusted values for the risk of death and ICU requirement were still statistically significant. Vitamin D deficiency can be considered as a predictor of poor outcomes and mortality in COVID‐19 patients. Therefore, checking serum 25 (OH) D on admission and taking vitamin D supplements according to the prophylactic or treatment protocols is recommended for all COVID‐19 patients.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".